Most state-of-the-art anomaly detection systems use deep learning based solutions for pattern recognition and discern deviations for such patterns. Typically, such techniques divide the time-series data into discrete time-series windows and create a reconstruction of such window inputs. The deviation of the reconstructed window from the input is used to generate an anomaly score, a continuous running value that indicates the likelihood of an anomaly. The anomaly prediction labels are generated using one of the various thresholding techniques. A category within these techniques uses light-weight online sequential extreme learning machines (OS-ELM) for their fast learning. One such memory-efficient technique of edge devices is the on-device learning anomaly detector, ONLAD [1]} that uses a combination of OS-ELM and autoencoders to generate window reconstructions. Other methods use generative models like variational autoencoders or GANs for the same task and have been shown to perform very well [2]}, [3]}. However, to fit such models in memory-constrained environments, we use model compression and GAN slimming respectively [4]}, [5]}, with their respective precision tradeoffs. Thus, our baselines include MAD-GAN with GAN Slimming [3]}, [5]}, autoencoder based USAD with model compression [2]}, [4]} and convolutional networks with CNN Slimming, CAE-M [10]}, [11]}. We also include other models with flexible model sizes like fully connected network based SlimGAN [12]}.
